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Dynamic policy analysis

Author

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  • Jaap Abbring

    (Institute for Fiscal Studies and Tinbergen Institute)

  • James Heckman

    (Institute for Fiscal Studies and University of Chicago)

Abstract

This chapter studies the microeconometric treatment-effect and structural approaches to dynamic policy evaluation. First, we discuss a reduced-form approach based on a sequential randomization or dynamic matching assumption that is popular in biostatistics. We then discuss two complementary approaches for treatments that are single stopping times and that allow for non- trivial dynamic selection on unobservables. The first builds on continuous-time duration and event-history models. The second extends the discrete-time dynamic discrete-choice literature.

Suggested Citation

  • Jaap Abbring & James Heckman, 2008. "Dynamic policy analysis," CeMMAP working papers CWP05/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:05/08
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    1. Jaap H. Abbring, 0000. "Mixed Hitting-Time Models," Tinbergen Institute Discussion Papers 07-057/3, Tinbergen Institute, revised 11 Aug 2009.
    2. James J. Heckman, 2008. "Econometric Causality," International Statistical Review, International Statistical Institute, vol. 76(1), pages 1-27, April.
    3. Heckman, James J. & Navarro, Salvador, 2007. "Dynamic discrete choice and dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 136(2), pages 341-396, February.
    4. Sumit K. Majumdar & Rabih Moussawi & Ulku Yaylacicegi, 2019. "Mergers and Wages in Digital Networks: a Public Interest Perspective," Journal of Industry, Competition and Trade, Springer, vol. 19(4), pages 583-615, December.
    5. Mroz, T. & Picone, G., 2011. "A Multiple State Duration Model with Endogenous Treatment," Health, Econometrics and Data Group (HEDG) Working Papers 11/19, HEDG, c/o Department of Economics, University of York.
    6. Michael Lechner & Ruth Miquel, 2010. "Identification of the effects of dynamic treatments by sequential conditional independence assumptions," Empirical Economics, Springer, vol. 39(1), pages 111-137, August.
    7. Majumdar, Sumit K., 2014. "Technology and wages: Why firms invest and what happens," Technology in Society, Elsevier, vol. 39(C), pages 44-54.
    8. Majumdar, Sumit K., 2016. "Debt and communications technology diffusion: Retrospective evidence," Research Policy, Elsevier, vol. 45(2), pages 458-474.
    9. Štefánik, Miroslav & Lafférs, Lukáš, 2024. "Supporting the right workplace experience: a dynamic evaluation of three activation programmes for young job seekers in Slovakia," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 58, pages 1-16.
    10. Majumdar, Sumit K., 2016. "R&D and the overseas earnings of Indian firms," Technology in Society, Elsevier, vol. 44(C), pages 104-111.
    11. Fernando Jiménez-Sáez & Jon Mikel Zabala-Iturriagagoitia & Jose Luis Zofío, 2013. "Who leads research productivity growth? Guidelines for R&D policy-makers," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(1), pages 273-303, January.
    12. Jaap H. Abbring, 2010. "Identification of Dynamic Discrete Choice Models," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 367-394, September.

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